The Power of the Noisy Channel: Unsupervised End-to-End Task-Oriented Dialogue with LLMs

arXiv:2404.15219v1 Announce Type: new
Abstract: Training task-oriented dialogue systems typically requires turn-level annotations for interacting with their APIs: e.g. a dialogue state and the system actions taken at each step. These annotations can be costly to produce, error-prone, and require both domain and annotation expertise. With advances in LLMs, we hypothesize unlabelled data and a schema definition are sufficient for building a working task-oriented dialogue system, completely unsupervised. Using only (1) a well-defined API schema (2) a set of unlabelled …

abstract advances annotation annotations apis arxiv cs.cl dialogue domain error expertise llms power state systems training type unsupervised